Adaptive 3D facial action intensity estimation and emotion recognition

Automatic recognition of facial emotion has been widely studied for various computer vision tasks (e.g. health monitoring, driver state surveillance and personalized learning). Most existing facial emotion recognition systems, however, either have not fully considered subject-independent dynamic features or were limited to 2D models, thus are not robust enough for real-life recognition tasks with subject variation, head movement and illumination change. Moreover, there is also lack of systematic research on effective newly arrived novel emotion class detection. To address these challenges, we present a real-time 3D facial Action Unit (AU) intensity estimation and emotion recognition system. It automatically selects 16 motion-based facial feature sets using minimal-redundancy–maximal-relevance criterion based optimization and estimates the intensities of 16 diagnostic AUs using feedforward Neural Networks and Support Vector Regressors. We also propose a set of six novel adaptive ensemble classifiers for robust classification of the six basic emotions and the detection of newly arrived unseen novel emotion classes (emotions that are not included in the training set). A distance-based clustering and uncertainty measures of the base classifiers within each ensemble model are used to inform the novel class detection. Evaluated with the Bosphorus 3D database, the system has achieved the best performance of 0.071 overall Mean Squared Error (MSE) for AU intensity estimation using Support Vector Regressors, and 92.2% average accuracy for the recognition of the six basic emotions using the proposed ensemble classifiers. In comparison with other related work, our research outperforms other state-of-the-art research on 3D facial emotion recognition for the Bosphorus database. Moreover, in on-line real-time evaluation with real human subjects, the proposed system also shows superior real-time performance with 84% recognition accuracy and great flexibility and adaptation for newly arrived novel (e.g. ‘contempt’ which is not included in the six basic emotions) emotion detection

Нейро-мережевий підхід до настройки нечітких баз знань на основі трендових і сполучених правил

Пропонується підхід до настройки експертних нечітких баз знань на основі розв’язання рівнянь нечітких відношень, що дозволяє уникнути злиття або селекції правил. Суть підходу полягає у побудові та навчанні min-max нейро-нечіткої мережі, ізоморфної лінгвістичним розв’язкам рівнянь нечітких відношень, яка дозволяє поетапно налаштовувати структуру і параметри трендових і сполучених правил

Data mining techniques for protein sequence analysis

This thesis concerns two areas of bioinformatics related by their role in protein structure and function: protein structure prediction and post translational modification of proteins. The dihedral angles Ψ and Φ are predicted using support vector regression. For the prediction of Ψ dihedral angles the addition of structural information is examined and the normalisation of Ψ and Φ dihedral angles is examined. An application of the dihedral angles is investigated. The relationship between dihedral angles and three bond J couplings determined from NMR experiments is described by the Karplus equation. We investigate the determination of the correct solution of the Karplus equation using predicted Φ dihedral angles. Glycosylation is an important post translational modification of proteins involved in many different facets of biology. The work here investigates the prediction of N-linked and O-linked glycosylation sites using the random forest machine learning algorithm and pairwise patterns in the data. This methodology produces more accurate results when compared to state of the art prediction methods. The black box nature of random forest is addressed by using the trepan algorithm to generate a decision tree with comprehensible rules that represents the decision making process of random forest. The prediction of our program GPP does not distinguish between glycans at a given glycosylation site. We use farthest first clustering, with the idea of classifying each glycosylation site by the sugar linking the glycan to protein. This thesis demonstrates the prediction of protein backbone torsion angles and improves the current state of the art for the prediction of glycosylation sites. It also investigates potential applications and the interpretation of these methods

Creating Persian-like music using computational intelligence

Dastgāh are modal systems in traditional Persian music. Each Dastgāh consists of a group of melodies called Gushé, classified in twelve groups about a century ago (Farhat, 1990). Prior to that time, musical pieces were transferred through oral tradition. The traditional music productions revolve around the existing Dastgāh, and Gushe pieces. In this thesis computational intelligence tools are employed in creating novel Dastgāh-like music.There are three types of creativity: combinational, exploratory, and transformational (Boden, 2000). In exploratory creativity, a conceptual space is navigated for discovering new forms. Sometimes the exploration results in transformational creativity. This is due to meaningful alterations happening on one or more of the governing dimensions of an item. In combinational creativity new links are established between items not previously connected. Boden stated that all these types of creativity can be implemented using artificial intelligence.Various tools, and techniques are employed, in the research reported in this thesis, for generating Dastgāh-like music. Evolutionary algorithms are responsible for navigating the space of sequences of musical motives. Aesthetical critics are employed for constraining the search space in exploratory (and hopefully transformational) type of creativity. Boltzmann machine models are applied for assimilating some of the mechanisms involved in combinational creativity. The creative processes involved are guided by aesthetical critics, some of which are derived from a traditional Persian music database.In this project, Cellular Automata (CA) are the main pattern generators employed to produce raw creative materials. Various methodologies are suggested for extracting features from CA progressions and mapping them to musical space, and input to audio synthesizers. The evaluation of the results of this thesis are assisted by publishing surveys which targeted both public and professional audiences. The generated audio samples are evaluated regarding their Dastgāh-likeness, and the level of creativity of the systems involved

Magnetic Resonance Imaging, texture analysis and regression techniques to non-destructively predict the quality characteristics of meat pieces

The quality of meat products is traditionally assessed by chemical or sensorial analysis, which are time consuming, need specialized technicians and destroy the products. The development of new technologies to monitor meat pieces using non-destructive methods in order to establish their quality is earning importance in the last years. An increasing number of studies have been carried out on meat pieces combining Magnetic Resonance Imaging (MRI), texture descriptors and regression techniques to predict several physico-chemical or sensorial attributes of the meat, mainly different types of pig ham and loins. In spite of the importance of the problem, the conclusions of these works are still preliminary because they only use the most classical texture descriptors and regressors instead of stronger methods, and because the methodology used to measure the performance is optimistic. In this work, we test a wide range of texture analysis techniques and regression methods using a realistic methodology to predict several physico-chemical and sensorial attributes of different meat pieces of Iberian pigs. The texture descriptors include statistical techniques, like Haralick descriptors, local binary patterns, fractal features and frequential descriptors, like Gabor or wavelet features. The regression techniques include linear regressors, neural networks, deep learning, support vector machines, regression trees, ensembles, boosting machines and random forests, among others. We developed experiments using 15 texture feature vectors, 28 regressors over 4 datasets of Iberian pig meat pieces to predict 39 physico-chemical and sensorial attributes, summarizing16,380 experiments. There is not any combination of texture vector and regressor which provides the best result for all attributes tested. Nevertheless, all these experiments provided the following conclusions: (1) the regressor performance, measured using the squared correlation (R2), is from good to excellent (above 0.5625) for 29 out of 39 attributes tested; (2) the WAPE (Weighted Absolute Percent Error) is lower than 2% for 32 out of 37 attributes; (3) the dispersion in computer predictions around the true attributes is lower or similar than the dispersion in the labeling expert’s for the majority of attributes (85%); and (4) differences between predicted and true values are not statistically significant for 29 out of 37 attributes using the Wilcoxon ranksum statistical test. We can conclude that these results provide a high reliability for an automatic system to predict the quality of meat pieces, which may operate on-line in the meat industries in the futureThe authors wish to acknowledge the funding received from the FEDER-MICCIN Infrastructure Research Project (UNEX-10-1E-402), Junta de Extremadura economic support for research group (GRU15173 and GRU15113), from the Xunta de Galicia (Centro singular de investigación de Galicia accreditation 2016–2019) and from the European Union (European Regional Development Fund — ERDF)S

Bioinformatics Applications Based On Machine Learning

The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems